The authors have nothing to disclose.

1. INS-1E Cells: Maintenance and Plating
<ol>
	<li>Maintain INS-1E cells in a humidified 37 &#730;C/5% CO2 incubator and cultured with RPMI 1640 medium supplemented with 5% (v/v) heat-inactivated fetal bovine serum, 2 mM L-glutamine, 10 mM HEPES, 1 mM sodium pyruvate, 100 U/mL Penicillin/Streptomycin solution, 50 &#181;M &#946;-mercaptoethanol. Culture cells in 10 mL complete RPMI 1640 medium (per plate), until they reach 80 - 90% confluence, when they can be trypsinized and passaged or used for the insulin secretion assay.</li>
	<li>Day 1: Aspirate media and wash cells once with 5 mL of pre-warmed PBS. Add 0.5 mL of trypsin (0.025%) diluted 1:1 in 0.5 mL PBS to trypsinize the cells and incubate for 3 - 4 min at 37 &#730;C. Deactivate trypsin by adding 9 mL complete media and transfer cells to a 15 mL centrifuge tube by pipetting.</li>
	<li>Pellet cells by centrifugation and re-suspend the cell pellet in approximately 5 mL fresh media.</li>
	<li>Take 10 &#181;L of the re-suspended cells and mix with 10 &#181;L Trypan Blue vital dye to check for cell viability. Count live and dead cells in 10 &#181;L of this mixture using a hemocytometer. Viability level should be above 90%. Dilute the re-suspended cells in fresh media to 1 million cells per mL.</li>
	<li>Seed 0.5 mL INS-1E cells per well in a poly-L-Lysine pre-coated 24-well plate, at a density of 500,000 cells/well.</li>
	<li>Day 2: Remove media 18 - 24 h after plating and add 500 &#181;L/well of fresh RPMI 1640 media. Incubate the cells for another 24 h to allow cells to fully recover from the prior passaging step. This additional time permits the cells to spread on the tissue culture plate.</li>
</ol>
2. Insulin Secretion Assay (Day 3)
<ol>
	<li>Prepare KRB buffer: 132.2 mM NaCl, 3.6 mM KCl, 5 mM NaHCO3, 0.5 mM NaH2PO4, 0.5 mM MgCl2, 1.5 mM CaCl2,&#160;and 0.001 g/mL bovine serum albumin (BSA), pH 7.4.</li>
	<li>Aspirate media from cells and wash twice with pre-warmed PBS.</li>
	<li>For the glucose starvation step, add 450 &#181;L/well KRB (containing BSA) without glucose for 1 h at 37 &#730;C/5% CO2.</li>
	<li>During the glucose starvation step, prepare serial dilutions of the drug(s) in KRB containing 200 mM Glucose (10x concentration).
	<ol>
		<li>Prepare drug at 10x the final concentration in KRB supplemented with 200 mM glucose (also 10x the final assay glucose concentration). If the drug stock (plus the added 10x glucose) is in DMSO, make sure DMSO percentage is kept consistent throughout the assay (ideally final percentage of less than 0.1% DMSO).</li>
		<li>For dopamine and quinpirole treatments, use a final assay drug concentration range of 100 &#181;M to 100 pM (from highest to lowest concentration), with the last point of the dose response containing no drug. For bromocriptine, use a final assay concentration range of 10 &#181;M to 10 pM, with the last point of the dose response being the drug-free control.</li>
	</ol>
	</li>
	<li>After glucose starvation, add drug serial dilutions to the assay to produce a dose response.</li>
	<li>Add 50 &#181;L/well of each serial dilution to the corresponding wells (total assay volume 500 &#181;L).</li>
	<li>For the glucose stimulation step, incubate cells with the respective drug serial dilutions (in the presence of 20 mM glucose) for 90 min at 37 &#730;C/5% CO2. Include a set of control wells: (1) stimulation with 20 mM glucose alone in the absence of any additional drug and (2) cells that are neither stimulated with drug nor glucose (which provides a basal rate of secretion).</li>
	<li>After the stimulation step, carefully remove the supernatants (use directly or store at 4 &#730;C). 
	NOTE: An additional 5 min gentle centrifugation step (600 x g, 1 min) may be introduced at this point to remove any remaining cells in the assay supernatants.</li>
</ol>
3. HTRF to Measure Insulin Secretion
<ol>
	<li>Dilute assay supernatants 1:10 in KRB (without BSA), preferably in clear 96-well plates.</li>
	<li>Prepare the insulin standard curve for the HTRF insulin assay (Table 1).</li>
</ol>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Standard stock solution 500 ng/ml</td>
			<td>Serial dilutions</td>
			<td>Working [insulin] ng/ml</td>
		</tr>
		<tr>
			<td>STD 7</td>
			<td>30 &#181;l Stock + 140 &#181;l KRB</td>
			<td>150</td>
		</tr>
		<tr>
			<td>STD 6</td>
			<td>30 &#181;l STD 7 + 45 &#181;l KRB</td>
			<td>60</td>
		</tr>
		<tr>
			<td>STD 5</td>
			<td>30 &#181;l STD 6 + 45 &#181;l KRB</td>
			<td>24</td>
		</tr>
		<tr>
			<td>STD 4</td>
			<td>30 &#181;l STD 5 + 45 &#181;l KRB</td>
			<td>9.6</td>
		</tr>
		<tr>
			<td>STD 3</td>
			<td>30 &#181;l STD 4 + 45 &#181;l KRB</td>
			<td>3.84</td>
		</tr>
		<tr>
			<td>STD 2</td>
			<td>30 &#181;l STD 3 + 45 &#181;l KRB</td>
			<td>1.54</td>
		</tr>
		<tr>
			<td>STD 1</td>
			<td>30 &#181;l STD 2 + 45 &#181;l KRB</td>
			<td>0.61</td>
		</tr>
		<tr>
			<td>STD 0</td>
			<td>45 &#181;l KRB</td>
			<td>0</td>
		</tr>
		<tr>
			<td colspan="3">Note: STD Stock is 500 ng/ml</td>
		</tr>
	</tbody>
</table>
Table 1. Serial dilutions to make the insulin standard curve.
<ol start="3">
	<li>Add the standard curve samples and the diluted assay supernatants to the HTRF plate. The measurement of secreted insulin by HTRF can be carried out in either a 96-well or a 384-well plate format, keeping in mind that the assay volume has to be re-adjusted. Use 10 &#181;L/well sample in 96-well white half-area plates or 5 &#181;L/well in a 384-well white low-volume, round-bottom plate (see Table of Materials).</li>
	<li>Prepare antibody mix in detection buffer (see Table of Materials) in a 1:2 donor (cryptate)/acceptor (XL-665) ratio. 
	NOTE: Further specific details about the HTRF assay are available from the manufacturer.</li>
	<li>Add antibody mix to the assay at 30 &#181;L/well (for a 96 well-plate assay format) or 15 &#181;L/well (for a 384-well plate assay format).</li>
	<li>Seal the plate and incubate at room temperature.</li>
	<li>Read plate after 2 h, 4 h, and/or overnight incubation with antibodies using the plate reader and the appropriate HTRF optic module (337 665 620 nm) (see Table of Materials and manufacturer&#39;s instructions). Set the integration start at 60 &#181;s and the integration time at 400 &#181;s. Use 200 flashes per well. 
	NOTE: These parameters were based on the use of our particular reader. The readout at 620 nm and 665 nm may vary between different instruments. This is one of the reasons we suggest using the 665/620 ratio. In calculating this ratio, any potential differences from reader to reader will be normalized and provide consistent values irrespective of the instrument used to measure HTRF.</li>
</ol>
4. Data Analysis and Normalization
<ol>
	<li>Calculate the insulin concentrations of the assay wells via extrapolation of ratiometric fluorescence readings (665 nm/620 nm) to a second order quadratic polynomial curve (Figure 2).</li>
</ol>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/57531/57531fig2.jpg" /> 
Figure 2: Insulin standard curve. Human insulin stock of known concentrations was used to generate the insulin standard curve. The resulting HTRF ratios (665 nm/ 620 nm) were plotted against the insulin concentrations. The data was best fit to a second order quadratic polynomial curve (R2 = 0.99996). This is a representative standard curve. Error bars = SEM. <a href="/files/ftp_upload/57531/57531fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="2">
	<li>With the extrapolated data as ng/mL of insulin secreted, normalize (insulin secreted in response to increasing ligand concentrations) to the average value of the % maximum insulin secretion assay wells (20 mM glucose alone condition).</li>
	<li>Use the curve fit (R2) from a single experiment to calculate the intraplate variation. Within the experiment, derived the individual R2 values from the intra-experimental duplicates, allowing calculation the standard error of the mean for the curve fit.</li>
	<li>For determining the interplate variation, use the data from at least three individual experiments to calculate the standard error of the mean for the R2 value of the collective curve.</li>
</ol>

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We thank Nicolas Pierre (Cisbio Bioassays) for helpful advice and Dr. Pierre Maechler (University of Geneva) for generously providing INS-1E cells. This work was supported by funding from the Department of Defense (grant PR141292 to Z.F.), and the John F. and Nancy A. Emmerling Fund of The Pittsburgh Foundation (to Z.F.).

The HTRF insulin assay described here offers a rapid, efficient system to measure insulin secretion from a cultured cell-based system. Among its most important advantages, this assay offers a low background signal due to the high signal-to-noise ratio. Additionally, we have confirmed that the HTRF signal is stable for extended periods of time (&#62;24 h)7. Nevertheless, since the insulin-binding monoclonal antibodies quickly reach binding saturation after addition to the assay, kinetics of antibody binding may be affected by ambient temperature or variations in secreted levels of insulin. Therefore, we read the assay plate at multiple time points (2 h, 4 h, and overnight) to precisely identify when the antibodies have reached full saturation and HTRF signal is most robust7. We have also recently shown the insulin antibodies do not significantly cross-react with proinsulin or c-peptide, suggesting that the assay is quite specific for mature insulin7.
An important step in the optimization of the assay involved establishing a broad dynamic range of insulin secretion. This dynamic range is defined as the difference in detected insulin during basal (unstimulated) versus stimulated insulin secretion. We find that an initial brief glucose starvation step (0 mM glucose, 1 h) preceding high glucose stimulation expands the dynamic range by priming the INS-1E cells to secrete more insulin during GSIS. In earlier work, we showed the assay&#39;s dynamic range to be from 0.17 ng/mL (limit of detection) to as high as ~110 ng/mL insulin with a signal to noise ratio of 10.027. Additionally, the amount of secreted insulin is dependent on initial cell number plated per well. We further optimized the assay by standardizing the passaging and handling of the INS-1E cells. Maintaining consistency in passaging schedule and plating in a specific plate type (e.g., 10 cm dishes) is essential for obtaining consistent secreted insulin levels.
Among potential sources of assay variability is bubbling in the assay solutions which interferes with the HTRF readout. An important source of these bubbles is the presence of bovine serum albumin (BSA) in the various KRB-based assay solutions. To troubleshoot this potential problem, reducing or even eliminating BSA significantly minimizes bubbles in the solution and therefore improves HTRF signal reliability. This is particularly important when generating the insulin standard curve and drug dilutions. Cell passage number-dependent differences in basal insulin secretion are another source of potential assay variability (data not shown). This is consistent with earlier work by Merglen and colleagues who also suggested that optimal insulin secretion is found between INS-1E cell passage numbers ~60 - 10015. Therefore, to minimize possible variability in insulin secretion related to cell passage number, we recommend limiting secretion assays to this well-defined cell passage range.
The primary limitation of the cell-based insulin secretion assay is the variability of the basal secretion intrinsic to the INS-1E cells. This may be related to the growing insulin-secreting cells outside of the physiological context of the pancreatic islet. Indeed, the other cell types within the islet including alpha and delta cells secrete numerous factors that may modulate basal and stimulated insulin secretion from beta cells in a local, paracrine manner25. The absence of these other islet cell types may disturb the tight regulation of insulin secretion, leading to greater variability than otherwise observed physiologically. Another potential limitation is the relatively small linear range of the assay, since insulin secreted in response to 20 mM glucose was almost saturating in some cases. As a result, in the undiluted cell supernatants, effects of insulin secretagogues on stimulated insulin secretion may be out of the linear portion of the calibration curve. This is, however, obviated by diluting the insulin-containing supernatant sufficiently to allow the insulin to fall into the linear range of the calibration curve.
We validated our assay by showing that agonism of dopamine D2-like receptors via dopamine, bromocriptine and quinpirole treatments inhibited GSIS in a concentration-dependent manner. These data allowed us to determine pharmacological properties of these drugs (i.e., IC50 and efficacy) in terms of their effects on GSIS. Our results are consistent with a growing body of evidence suggesting an important role for dopamine and dopaminergic signaling outside of the central nervous system20,21,22,26,27. Notably, our findings showing that the dopamine D2-receptor agonist bromocriptine potently inhibits GSIS, yet was recently approved by the FDA to treat type 2 diabetes24, raises the important question: how can this drug treat type 2 diabetes (T2D) if it lowers insulin secretion, at least acutely? One potential explanation may be related to bromocriptine&#39;s capacity to incur &#34;beta cell rest.&#34; According to this hypothesis, since one of the cardinal features of T2D is an overall oversecretion of insulin, that ultimately may contribute to beta cell failure28. Moreover, we suggest that insulin oversecretion during T2D may also exacerbate insulin resistance by diminishing insulin sensitivity of organs including skeletal muscle, liver and adipose tissue. Thus, blocking or diminishing GSIS may result in gradual resensitization of these target tissues, making them more responsive to insulin and thus improving insulin sensitivity. Having an assay system amenable to high-throughput screening opens the door to future work to both dissect the intracellular signaling pathways mediated by dopamine D2-like receptors in pancreatic beta cells that are responsible for paracrine/autocrine GSIS inhibition as well as to further elucidate mechanisms by which drugs like bromocriptine may improve symptoms of diabetes.
The development of a cell-based insulin secretion assay that takes advantage of HTRF technology for insulin secretion and represents a significant advance in rapid, and efficient detection of insulin at significantly diminished cost relative and/or greater safety compared to older insulin detection assays relying on RIA and ELISA-based approaches. Indeed, though ELISA and RIA insulin assays are comparably sensitive and specific to the HTRF insulin assay, the homogenous nature of the detection, eliminates numerous additional washing steps present in these other assay systems7. Furthermore, the relative stability of the HTRF signal provides another key advantage for reliable insulin measurement.
Taken together, the HTRF insulin secretion assay has the capability of rapidly discriminating between the pharmacological properties of multiple drugs in the context of cellular insulin secretion. Moreover, this assay&#39;s relatively ease of use and robust signal make it amenable for high-throughput screening.

The regulation of energy metabolism is fine-tuned by a major anabolic hormone, insulin. Insulin is synthesized and released by pancreatic beta cells in response to increased extracellular glucose levels. The released insulin triggers the uptake of glucose by insulin-sensitive tissues1,2. Physiologically, this is linked to the elevation of glucose concentration after a meal, followed by secretion of insulin to regulate glucose uptake. Disturbances in glucose homeostasis lead to metabolic impairments culminating in insulin resistance and ultimately in the onset of type 2 diabetes2,3,4.
Although insulin secretion has been extensively studied, its regulatory mechanisms remain poorly understood. A critical area of investigation has been identification of novel modulators of insulin secretion by beta cells5,6,7,8. These studies require a better understanding of the coupling relationship between glucose stimulation and insulin secretion. Therefore, the ability to accurately monitor and quantify the levels of glucose-stimulated insulin secretion (GSIS) has been essential. To date, however, only a limited number of methods were available to allow quantification of GSIS using insulin-secreting cell lines and/or pancreatic islets. One is radioimmunoassay (RIA), which utilizes radioisotope-tagged insulin and antibodies. The main limitations of this approach include safety issues due to the handling and disposal of radioactive materials. Additionally, this method is labor-intensive, involving multiple long washing and incubation steps. Enzyme-linked immunosorbent assay (ELISA) is another costly and labor-intensive approach that utilizes antibodies for insulin detection. Variation in antibody affinities and in the efficiency of recognizing insulin are limiting factors of this method and can affect the reproducibility of the results. Neither ELISA nor RIA was designed for high-throughput experiments. AlphaScreen is a homogeneous assay used for detecting and measuring levels of insulin secretion. AlphaScreen technology is based on the conversion of ambient oxygen into an excited oxygen singlet state that can react with chemiluminescent species, resulting in the generation of chemiluminescence. Because the assay is homogenous, many of the washing steps associated with RIA and ELISA are eliminated. However, the instability of the signal due to the nature of the reaction is a limiting factor that may affect the readout of the assay. (TR-PINCER, developed by Heyduk and colleagues9, is another homogenous approach to insulin measurement based on the binding of two separate antibodies to different epitopes on the insulin molecule. The antibodies are each chemically linked to double stranded DNA with short complementary single stranded DNA overhangs. Binding of the antibodies to insulin brings them together and leads to a double stranded DNA duplex. Each antibody is also associated with a respective donor or acceptor fluorophore, and the association of the DNA duplex brings together these fluorophores to generate F&#246;rster resonance energy transfer (FRET). One potential limitation of TR-PINCER, however, rests with the FRET itself. The inability to rapidly dissipate background fluorescence during the FRET reaction may lead to relatively high levels of background fluorescence and a low signal to noise ratio within the assay. Therefore, a need still exists for a reliable, robust, and cost-effective assay for quantifying GSIS in a high-throughput manner.
Recent advances in biophysics have culminated in the development of a homogeneous time-resolved fluorescence energy resonance transfer (HTRF) based assay. Specifically, while the energy transfer within the assay may be described as FRET-based, more accurately, HTRF relies on luminescence energy resonance transfer (LRET)10 which is the non-radiative transfer of energy between the donor and acceptor species11,12,13. This distinction is important, since the timing of a fluorescence or quenching based FRET interaction is much different than it is for LRET, though the same types of gating can be used for FRET and LRET. Moreover, the use of rare earth lanthanide cryptate compounds such as europium or terbium cryptate in HTRF produces long fluorescence half-lives12,14. This offers the unique advantage of the introduction of a time delay (&#181;sec) between donor excitation and the measurement of emission from the acceptor (i.e., time-resolved assay). This time delay allows for sufficient time for the background fluorescence to dissipate prior to measurement of acceptor emission fluorescence. Consequently, the readout is free of non-specific fluorescence and thus, a high signal-to-noise ratio is achieved (Figure 1). Furthermore, the homogenous nature of HTRF eliminates the need for washing steps to wash off the unbound species, making the assay much more rapid than ELISA or RIA-based methods.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57531/57531fig1.jpg" /> 
Figure 1: Schematic of the mechanism for HTRF insulin detection. Two independently generated monoclonal antibodies specifically recognize and bind to insulin at separate sites. These antibodies are conjugated to either the europium cryptate donor or the XL665 acceptor. Excitation of the donor at 337 nm results in emission at 620 nm. The resulting energy transfer causes XL665 to emit at a longer wavelength, 665 nm. <a href="/files/ftp_upload/57531/57531fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Here, we provide a detailed protocol for using an HTRF-based approach to determine the levels of GSIS from INS-1E cells, a well-established insulin-secreting beta cell-derived rat insulinoma cell line15. In addition, this assay may be used for identifying the pharmacological profile of molecular regulators of insulin secretion. We apply this HTRF-based insulin assay to examine dopamine D2-like receptor regulation of GSIS. Increasing studies have revealed that the neurotransmitter dopamine is an important regulator of GSIS8,16,17,18,19,20,21,22. Dopamine affects GSIS in a negative autocrine/paracrine manner via actions on the dopamine D2-like receptors (D2, D3, D4 receptors) expressed at the surface of beta pancreatic cells8,16,19. Using this assay, we confirm dopamine&#39;s role as a negative regulator of GSIS and demonstrate that the dopamine D2-like receptor agonists bromocriptine and quinpirole also reduce GSIS.

<table border="0" cellpadding="0" cellspacing="0" width="773">
	<tbody>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">96-well white half area plate</td>
			<td style="width:104px;">Greiner Bio-One</td>
			<td style="width:139px;">82050-042</td>
			<td style="width:315px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">384-well white low-volume, round-bottom plate</td>
			<td style="width:104px;">Corning</td>
			<td>15100157</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Insulin High Range Kit</td>
			<td style="width:104px;">Cisbio Bioassays</td>
			<td style="width:139px;">62IN1PEH&#160;</td>
			<td>10,000 test HTRF-based insulin assay kit</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">PHERAstar FSX plate reader</td>
			<td style="width:104px;">BMG Labtech</td>
			<td>FSX</td>
			<td>Plate reader adapted for HTRF-based assay readings</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Plate sealers</td>
			<td style="width:104px;">Fisher Scientific</td>
			<td style="width:139px;">DY992</td>
			<td>To seal plate while antibodies incubate</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Hemocytometer</td>
			<td style="width:104px;">Hausser Scientific</td>
			<td style="width:139px;">3100</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">RPMI Medium 1640 (1x)</td>
			<td style="width:104px;">Gibco&#160;</td>
			<td style="width:139px;">11875-093</td>
			<td>[+] L-glutamine</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Trypsin 0.05%&#160;</td>
			<td style="width:104px;">Corning</td>
			<td style="width:139px;">25-052-CI</td>
			<td>0.53 mM EDTA, (-) sodium bicarbonate</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">DPBS (Dulbecco&#39;s phosphate buffered saline)</td>
			<td style="width:104px;">Corning</td>
			<td style="width:139px;">21-031-CV</td>
			<td>Without calcium and magnesium</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Fetal Bovine Serum</td>
			<td style="width:104px;">Corning</td>
			<td style="width:139px;">35-010-CV</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">HEPES</td>
			<td style="width:104px;">Gibco&#160;</td>
			<td style="width:139px;">156-30-080</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Sodium pyruvate</td>
			<td style="width:104px;">Gibco&#160;</td>
			<td style="width:139px;">11360070</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Penicillin/Streptomycin solution 100x</td>
			<td style="width:104px;">Corning</td>
			<td style="width:139px;">30-002 CT</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">2-mercaptoethanol</td>
			<td style="width:104px;">Sigma</td>
			<td style="width:139px;">M1348</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Trypan Blue Stain (0.4%)</td>
			<td style="width:104px;">Gibco&#160;</td>
			<td style="width:139px;">15250-061</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Dopamine hydrochloride</td>
			<td style="width:104px;">Sigma</td>
			<td style="width:139px;">8502</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Bromocriptine mesylate</td>
			<td style="width:104px;">Tocris</td>
			<td style="width:139px;">427</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">(-)-Quinpirole hydrochloride</td>
			<td style="width:104px;">Tocris</td>
			<td style="width:139px;">1061</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Bovine Serum Albumin</td>
			<td style="width:104px;">Calbiochem</td>
			<td style="width:139px;">12659</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Poly-L-Lysine</td>
			<td style="width:104px;">Sigma</td>
			<td style="width:139px;">P4832</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Glucose</td>
			<td style="width:104px;">Sigma</td>
			<td style="width:139px;">G7021</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">DMSO (Dimethyl sulfoxide)</td>
			<td style="width:104px;">Sigma</td>
			<td style="width:139px;">276855</td>
			<td></td>
		</tr>
	</tbody>
</table>

homogeneous time-resolved f&#246;rster resonance energy transfer-based assay for detection of insulin secretion

The detection of insulin secretion is critical for elucidating mechanisms of regulated secretion as well as in studies of metabolism. Though numerous insulin assays have existed for decades, the recent advent of homogeneous time-resolved F&#246;rster Resonance Energy Transfer (HTRF) technology has significantly simplified these measurements. This is a rapid, cost-effective, reproducible, and robust optical assay reliant upon antibodies conjugated to bright fluorophores with long lasting emission which facilitates time-resolved F&#246;rster Resonance Energy Transfer. Moreover, HTRF insulin detection is amenable for the development of high-throughput screening assays. Here we use HTRF to detect insulin secretion in INS-1E cells, a rat insulinoma-derived cell line. This allows us to estimate basal levels of insulin and their changes in response to glucose stimulation. In addition, we use this insulin detection system to confirm the role of dopamine as a negative regulator of glucose-stimulated insulin secretion (GSIS). In a similar manner, other dopamine D2-like receptor agonists, quinpirole, and bromocriptine, reduce GSIS in a concentration-dependent manner. Our results highlight the utility of the HTRF insulin assay format in determining the role of numerous drugs in GSIS and their pharmacological profiles.

We validated our insulin HTRF assay by generating an insulin standard curve using purified human insulin standards of predefined concentrations (Figure 2). Generation of the standard curve permitted us to extrapolate the ratiometric fluorescence readings and thus to determine the secreted insulin levels in response to the drug treatments (Figure 2). Intraplate variation for curve fitting was minimal (R2 = 0.99996 &#177; 5.0 x 10-5) as was the interplate variation (R2 = 0.947 &#177; 5.5 x 10-5; n = 3 plates).
We next determined the basal levels of insulin secretion from INS-1E cells. Using a seeding density of 5 x 105 cells per well in a 24-well plate format, we found that cells secreted 54.53 &#177; 2.2 ng/mL of insulin in the absence of glucose over a duration of 90 min incubation in a static bath. Addition of 20 mM glucose to stimulate GSIS resulted in a significant enhancement in GSIS (112.4 &#177; 5.2 ng/mL insulin; p &#60;0.0001) (Figure 3).
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/57531/57531fig3.jpg" /> 
Figure 3: Estimation of basal versus stimulated insulin secretion. INS-1E cells were treated in the absence or the presence of high glucose (20 mM) stimulation for 90 min. A doubling in insulin secretion levels was detected in response to high glucose stimulation relative to the unstimulated cells (basal secretion condition; **** p &#60;0 .0001, two-tailed unpaired t-test). N = 5; all assays were carried out in triplicates. Error bars = SEM. <a href="/files/ftp_upload/57531/57531fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
To investigate the role of dopaminergic D2-like receptor stimulation in GSIS within our INS-1E cell system, we incubated cells in the presence of D2-like receptor agonist drugs (dopamine, quinpirole, and bromocriptine) during the 90-min stimulation period with 20 mM glucose (Figure 4).
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/57531/57531fig4.jpg" /> 
Figure 4: HTRF detection of dopamine D2-like receptor agonist inhibition of GSIS. INS-1E cells were treated with increasing concentrations of dopamine D2-like receptor agonists for 90 min in the presence of high glucose (20 mM; 37 &#730;C). (A) Dopamine inhibited GSIS with an IC50= 1.78 &#177; 3.9 &#181;M (R2&#160;= 0.4355). (B) Bromocriptine was more potent than dopamine in reducing GSIS (IC50&#160;= 13.9 &#177; 2.4 nM, R2&#160;= 0.7501). (C) Quinpirole&#39;s potency was similar to dopamine (IC50&#160;= 3.3 &#177; 3 &#181;M, R2&#160;= 0.3617). All data were best fit to a three-parameter fit sigmoidal curve. The &#39;0 mM Glucose&#39; point indicates basal insulin secretion by the INS-1E cells in the absence of high glucose (20 mM) stimulation. N &#62;3; all assays were carried out in triplicates on separate experimental days. Error bars = SEM. <a href="/files/ftp_upload/57531/57531fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The efficacy and potency of these agonist drugs was determined by graphing the insulin secretion curves. We found that in the absence of stimulation with high glucose, the INS-1E cells continued to secrete insulin, albeit to a markedly lower degree (indicated by the 0 mM condition), consistent with earlier reports15. Our results demonstrated that dopamine, the endogenous agonist of D2-like receptors, inhibited GSIS in a concertation-dependent manner (DA IC50&#160;= 1.78 &#177; 3.9 &#181;M; Figure 4A). In contrast, bromocriptine, a drug approved to treat type 2 diabetes23,24, was markedly more potent than dopamine in producing GSIS inhibition (IC50&#160;= 13.9 &#177; 2.4 nM; Figure 4B). Lastly, though less efficacious than bromocriptine, quinpirole exhibited similar potency to dopamine in GSIS inhibition (IC50&#160;= 3.3 &#177; 3 &#181;M; Figure 4C).

Watch this Scientific Journal Video about Homogeneous Time-resolved FÃƒÂ¶rster Resonance Energy Transfer-based Assay for Detection of Insulin Secretion at JoVE.com

Homogeneous Time-resolved F&#246;rster Resonance Energy Transfer-based Assay for Detection of Insulin Secretion

Here, we present homogeneous time resolved FRET (HTRF) as an efficient method for rapid detection of insulin secreted from cells.

The detection of insulin secretion is critical for elucidating mechanisms of regulated secretion as well as in studies of metabolism. Though numerous ...

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9.1K Views.  University of Pittsburgh. Here, we present homogeneous time resolved FRET (HTRF) as an efficient method for rapid detection of insulin secreted from cells.

Video: Homogeneous Time-resolved Förster Resonance Energy Transfer-based Assay for Detection of Insulin Secretion

Production and Detection of Reactive Oxygen Species (ROS) in Cancers

Reactive oxygen species include a number of molecules that damage DNA and RNA and oxidize proteins and lipids (lipid peroxydation). These reactive molecules contain an oxygen and include H2O2 (hydrogen peroxide), NO (nitric oxide), O2- (oxide anion), peroxynitrite (ONOO-), hydrochlorous acid (HOCl), and hydroxyl radical (OH-).
Oxidative species are produced not only under pathological situations (cancers, ischemic/reperfusion, neurologic and cardiovascular pathologies, infectious diseases, inflammatory diseases 1, autoimmune diseases 2, etc&hellip;) but also during physiological (non-pathological) situations such as cellular metabolism 3, 4. Indeed, ROS play important roles in many cellular signaling pathways (proliferation, cell activation 5, 6, migration 7 etc..). ROS can be detrimental (it is then referred to as "oxidative and nitrosative stress") when produced in high amounts in the intracellular compartments and cells generally respond to ROS by upregulating antioxidants such as superoxide dismutase (SOD) and catalase (CAT), glutathione peroxidase (GPx) and glutathione (GSH) that protects them by converting dangerous free radicals to harmless molecules (i.e. water). Vitamins C and E have also been described as ROS scavengers (antioxidants).
Free radicals are beneficial in low amounts 3. Macrophage and neutrophils-mediated immune responses involve the production and release of NO, which inhibits viruses, pathogens and tumor proliferation 8. NO also reacts with other ROS and thus, also has a role as a detoxifier (ROS scavenger). Finally NO acts on vessels to regulate blood flow which is important for the adaptation of muscle to prolonged exercise 9, 10. Several publications have also demonstrated that ROS are involved in insulin sensitivity 11, 12.
Numerous methods to evaluate ROS production are available. In this article we propose several simple, fast, and affordable assays; these assays have been validated by many publications and are routinely used to detect ROS or its effects in mammalian cells. While some of these assays detect multiple ROS, others detect only a single ROS.

Production and Detection of Reactive Oxygen Species ROS in Cancers

Here we propose simple methods to test and evaluate the presence of reactive oxygen species in cells.

Reactive oxygen species include a number of molecules that damage DNA and RNA and oxidize proteins and lipids (lipid peroxydation). These reactive ...

Improving IV Insulin Administration in a Community Hospital

Diabetes mellitus is a major independent risk factor for increased morbidity and mortality in the hospitalized patient, and elevated blood glucose concentrations, even in non-diabetic patients, predicts poor outcomes.1-4 The 2008 consensus statement by the American Association of Clinical Endocrinologists (AACE) and the American Diabetes Association (ADA) states that "hyperglycemia in hospitalized patients, irrespective of its cause, is unequivocally associated with adverse outcomes."5 It is important to recognize that hyperglycemia occurs in patients with known or undiagnosed diabetes as well as during acute illness in those with previously normal glucose tolerance.
The Normoglycemia in Intensive Care Evaluation-Survival Using Glucose Algorithm Regulation (NICE-SUGAR) study involved over six thousand adult intensive care unit (ICU) patients who were randomized to intensive glucose control or conventional glucose control.6 Surprisingly, this trial found that intensive glucose control increased the risk of mortality by 14% (odds ratio, 1.14; p=0.02). In addition, there was an increased prevalence of severe hypoglycemia in the intensive control group compared with the conventional control group (6.8% vs. 0.5%, respectively; p&lt;0.001). From this pivotal trial and two others,7,8 Wyoming Medical Center (WMC) realized the importance of controlling hyperglycemia in the hospitalized patient while avoiding the negative impact of resultant hypoglycemia. 
Despite multiple revisions of an IV insulin paper protocol, analysis of data from usage of the paper protocol at WMC shows that in terms of achieving normoglycemia while minimizing hypoglycemia, results were suboptimal. Therefore, through a systematical implementation plan, monitoring of patient blood glucose levels was switched from using a paper IV insulin protocol to a computerized glucose management system. By comparing blood glucose levels using the paper protocol to that of the computerized system, it was determined, that overall, the computerized glucose management system resulted in more rapid and tighter glucose control than the traditional paper protocol. Specifically, a substantial increase in the time spent within the target blood glucose concentration range, as well as a decrease in the prevalence of severe hypoglycemia (BG &lt; 40 mg/dL), clinical hypoglycemia (BG &lt; 70 mg/dL), and hyperglycemia (BG &gt; 180 mg/dL), was witnessed in the first five months after implementation of the computerized glucose management system. The computerized system achieved target concentrations in greater than 75% of all readings while minimizing the risk of hypoglycemia. The prevalence of hypoglycemia (BG &lt; 70 mg/dL) with the use of the computer glucose management system was well under 1%.

When compared to the previous paper protocol, implementation of a computerized glucose management system results in a substantial increase in blood glucose concentration measurements within the target range. Using a computerized glucose management system to monitor blood glucose levels, decreases in severe hypoglycemia (&lt;40 mg/dL), clinical hypoglycemia (&lt;70 mg/dL), and hyperglycemia (&gt;180 mg/dL) also can be observed.

Diabetes mellitus is a major independent risk factor for increased morbidity and mortality in the hospitalized patient, and elevated blood glucose ...

Cerenkov Luminescence Imaging (CLI) for Cancer Therapy Monitoring

In molecular imaging, positron emission tomography (PET) and optical imaging (OI) are two of the most important and thus most widely used modalities1-3. PET is characterized by its excellent sensitivity and quantification ability while OI is notable for non-radiation, relative low cost, short scanning time, high throughput, and wide availability to basic researchers. However, both modalities have their shortcomings as well. PET suffers from poor spatial resolution and high cost, while OI is mostly limited to preclinical applications because of its limited tissue penetration along with prominent scattering optical signals through the thickness of living tissues.
 Recently a bridge between PET and OI has emerged with the discovery of Cerenkov Luminescence Imaging (CLI)4-6. CLI is a new imaging modality that harnesses Cerenkov Radiation (CR) to image radionuclides with OI instruments. Russian Nobel laureate Alekseyevich Cerenkov and his colleagues originally discovered CR in 1934. It is a form of electromagnetic radiation emitted when a charged particle travels at a superluminal speed in a dielectric medium7,8. The charged particle, whether positron or electron, perturbs the electromagnetic field of the medium by displacing the electrons in its atoms. After passing of the disruption photons are emitted as the displaced electrons return to the ground state. For instance, one 18F decay was estimated to produce an average of 3 photons in water5. 
 Since its emergence, CLI has been investigated for its use in a variety of preclinical applications including in vivo tumor imaging, reporter gene imaging, radiotracer development, multimodality imaging, among others4,5,9,10,11. The most important reason why CLI has enjoyed much success so far is that this new technology takes advantage of the low cost and wide availability of OI to image radionuclides, which used to be imaged only by more expensive and less available nuclear imaging modalities such as PET.
 Here, we present the method of using CLI to monitor cancer drug therapy. Our group has recently investigated this new application and validated its feasibility by a proof-of-concept study12. We demonstrated that CLI and PET exhibited excellent correlations across different tumor xenografts and imaging probes. This is consistent with the overarching principle of CR that CLI essentially visualizes the same radionuclides as PET. We selected Bevacizumab (Avastin; Genentech/Roche) as our therapeutic agent because it is a well-known angiogenesis inhibitor13,14. Maturation of this technology in the near future can be envisioned to have a significant impact on preclinical drug development, screening, as well as therapy monitoring of patients receiving treatments.

Cerenkov Luminescence Imaging CLI for Cancer Therapy Monitoring

Use of Cerenkov Luminescence Imaging (CLI) for monitoring preclinical cancer treatment is described here. This method takes advantage of Cerenkov Radiation (CR) and optical imaging (OI) to visualize radiolabeled probes and thus provides an alternative to PET in preclinical therapeutic monitoring and drug screening.

In  molecular imaging, positron emission tomography (PET) and optical imaging (OI)  are two of the most important and thus most widely used modalities1-3. ...

Coculture Analysis of Extracellular Protein Interactions Affecting Insulin Secretion by Pancreatic Beta Cells

Interactions between cell-surface proteins help coordinate the function of neighboring cells. Pancreatic beta cells are clustered together within pancreatic islets and act in a coordinated fashion to maintain glucose homeostasis. It is becoming increasingly clear that interactions between transmembrane proteins on the surfaces of adjacent beta cells are important determinants of beta-cell function.
Elucidation of the roles of particular transcellular interactions by knockdown, knockout or overexpression studies in cultured beta cells or in vivo necessitates direct perturbation of mRNA and protein expression, potentially affecting beta-cell health and/or function in ways that could confound analyses of the effects of specific interactions. These approaches also alter levels of the intracellular domains of the targeted proteins and may prevent effects due to interactions between proteins within the same cell membrane to be distinguished from the effects of transcellular interactions.
Here a method for determining the effect of specific transcellular interactions on the insulin secreting capacity and responsiveness of beta cells is presented. This method is applicable to beta-cell lines, such as INS-1 cells, and to dissociated primary beta cells. It is based on coculture models developed by neurobiologists, who found that exposure of cultured neurons to specific neuronal proteins expressed on HEK293 (or COS) cell layers identified proteins important for driving synapse formation. Given the parallels between the secretory machinery of neuronal synapses and of beta cells, we reasoned that beta-cell functional maturation might be driven by similar transcellular interactions. We developed a system where beta cells are cultured on a layer of HEK293 cells expressing a protein of interest. In this model, the beta-cell cytoplasm is untouched while extracellular protein-protein interactions are manipulated. Although we focus here primarily on studies of glucose-stimulated insulin secretion, other processes can be analyzed; for example, changes in gene expression as determined by immunoblotting or qPCR.

Transcellular protein interactions are important determinants of pancreatic beta-cell function. Detailed here is a method&#x2014;adapted from a coculture model of synaptogenesis&#x2014;for investigating how specific transmembrane proteins influence insulin secretion. Transfected HEK293 cells express proteins of interest; beta cells do not need to be transfected or otherwise directly perturbed.

Interactions between cell-surface proteins help coordinate the function of neighboring cells. Pancreatic beta cells are clustered together within ...

Simultaneous PET/MRI Imaging During Mouse Cerebral Hypoxia-ischemia

Dynamic changes in tissue water diffusion and glucose metabolism occur during and after hypoxia in cerebral hypoxia-ischemia reflecting a bioenergetics disturbance in affected cells. Diffusion weighted magnetic resonance imaging (MRI) identifies regions that are damaged, potentially irreversibly, by hypoxia-ischemia. Alterations in glucose utilization in the affected tissue may be detectable by positron emission tomography (PET) imaging of 2-deoxy-2-(18F)fluoro-&#7429;-glucose ([18F]FDG) uptake. Due to the rapid and variable nature of injury in this animal model, acquisition of both modes of data must be performed simultaneously in order to meaningfully correlate PET and MRI data. In addition, inter-animal variability in the hypoxic-ischemic injury due to vascular differences limits the ability to analyze multi-modal data and observe changes to a group-wise approach if data is not acquired simultaneously in individual subjects. The method presented here allows one to acquire both diffusion-weighted MRI and [18F]FDG uptake data in the same animal before, during, and after the hypoxic challenge in order to interrogate immediate physiological changes.

The method presented here uses simultaneous positron emission tomography and magnetic resonance imaging. In the cerebral hypoxia-ischemia model, dynamic changes in diffusion and glucose metabolism occur during and after injury. The evolving and irreproducible damage in this model necessitates simultaneous acquisition if meaningful multi-modal imaging data are to be acquired.

Dynamic changes in tissue water diffusion and glucose metabolism occur during and after hypoxia in cerebral hypoxia-ischemia reflecting a bioenergetics ...

Combined Intravital Microscopy and Contrast-enhanced Ultrasonography of the Mouse Hindlimb to Study Insulin-induced Vasodilation and Muscle Perfusion

It has been demonstrated that insulin's vascular actions contribute to regulation of insulin sensitivity. Insulin's effects on muscle perfusion regulate postprandial delivery of nutrients and hormones to insulin-sensitive tissues. We here describe a technique for combining intravital microscopy (IVM) and contrast-enhanced ultrasonography (CEUS) of the adductor compartment of the mouse hindlimb to simultaneously visualize muscle resistance arteries and perfusion of the microcirculation in vivo. Simultaneously assessing insulin's effect at multiple levels of the vascular tree is important to study relationships between insulin's multiple vasoactive effects and muscle perfusion. Experiments in this study were performed in mice. First, the tail vein cannula is inserted for the infusion of anesthesia, vasoactive compounds and ultrasound contrast agent (lipid-encapsulated microbubbles). Second, a small incision is made in the groin area to expose the arterial tree of the adductor muscle compartment. The ultrasound probe is then positioned at the contralateral upper hindlimb to view the muscles in cross-section. To assess baseline parameters, the arterial diameter is assessed and microbubbles are subsequently infused at a constant rate to estimate muscle blood flow and microvascular blood volume (MBV). When applied before and during a hyperinsulinemic-euglycemic clamp, combined IVM and CEUS allow assessment of insulin-induced changes of arterial diameter, microvascular muscle perfusion and whole-body insulin sensitivity. Moreover, the temporal relationship between responses of the microcirculation and the resistance arteries to insulin can be quantified. It is also possible to follow-up the mice longitudinally in time, making it a valuable tool to study changes in vascular and whole-body insulin sensitivity.

Insulin-induced vasodilation regulates muscle perfusion and increases the microvascular surface area (microvascular recruitment) available for solute exchange between blood and tissue interstitium. Combined intravital microscopy and contrast-enhanced ultrasonography is presented to simultaneously assess insulin&#39;s action at the larger vessels and the microcirculation in vivo.

It has been demonstrated that insulin's vascular actions contribute to regulation of insulin sensitivity. Insulin's effects on muscle perfusion regulate ...

Cardiac Magnetic Resonance Imaging at 7 Tesla

CMR at an ultra-high field (magnetic field strength B0&#160;&#8805; 7 Tesla) benefits from the signal-to-noise ratio (SNR) advantage inherent at higher magnetic field strengths and potentially provides improved signal contrast and spatial resolution. While promising results have been achieved, ultra-high field CMR is challenging due to energy deposition constraints and physical phenomena such as transmission field non-uniformities and magnetic field inhomogeneities. In addition, the magneto-hydrodynamic effect renders the synchronization of the data acquisition with the cardiac motion difficult. The challenges are currently addressed by explorations into novel magnetic resonance technology. If all impediments can be overcome, ultra-high field CMR may generate new opportunities for functional CMR, myocardial tissue characterization, microstructure imaging or metabolic imaging. Recognizing this potential, we show that multi-channel radio frequency (RF) coil technology tailored for CMR at 7 Tesla together with higher order B0 shimming and a backup signal for cardiac triggering facilitates high fidelity functional CMR. With the proposed setup, cardiac chamber quantification can be accomplished in examination times similar to those achieved at lower field strengths. To share this experience and to support the dissemination of this expertise, this work describes our setup and protocol tailored for functional CMR at 7 Tesla.

The sensitivity gain inherent to ultrahigh field magnetic resonance holds promise for high spatial resolution imaging of the heart. Here, we describe a protocol customized for functional cardiovascular magnetic resonance (CMR) at 7 Tesla using an advanced multi-channel radio-frequency coil, magnetic field shimming and a triggering concept.

CMR at an ultra-high field (magnetic field strength B0&#160;&#8805; 7 Tesla) benefits from the signal-to-noise ratio (SNR) advantage inherent at higher ...

Studying the Hypothalamic Insulin Signal to Peripheral Glucose Intolerance with a Continuous Drug Infusion System into the Mouse Brain

Insulin regulates systematic metabolism in the hypothalamus and the peripheral insulin response. An inflammatory reaction in peripheral adipose tissues contributes to type 2 diabetes mellitus (T2DM) development and appetite regulation in the hypothalamus. Chemokine CCL5 and C-C chemokine receptor type 5 (CCR5) levels have been suggested to mediate arteriosclerosis and glucose intolerance in type 2 diabetes mellitus (T2DM). In addition, CCL5 plays a neuroendocrine role in the hypothalamus by regulating food intake and body temperature, thus, prompting us to investigate its function in hypothalamic insulin signaling and the regulation of peripheral glucose metabolism.
The micro-osmotic pump brain infusion system is a quick and precise way to manipulate CCL5 function and study its effect in the brain. It also provides a convenient alternative approach to generating a transgenic knockout animal. In this system, CCL5 signaling was blocked by intracerebroventricular (ICV) infusion of its antagonist, MetCCL5, using a micro-osmotic pump. The peripheral glucose metabolism and insulin responsiveness was detected by the Oral Glucose Tolerance Test (OGTT) and Insulin Tolerance Test (ITT). Insulin signaling activity was then analyzed by protein blot from tissue samples derived from the animals.
After 7-14 days of MetCCL5 infusion, the glucose metabolism and insulin responsiveness was impaired in mice, as seen in the results of the OGTT and ITT. The IRS-1 serine302 phosphorylation was increased and the Akt activity was reduced in mice hypothalamic neurons following CCL5 inhibition. Altogether, our data suggest that blocking CCL5 in the mouse brain increases the phosphorylation of IRS-1 S302 and interrupts hypothalamic insulin signaling, leading to a decrease in insulin function in peripheral tissues as well as the impairment of glucose metabolism.

This protocol studies the role of chemokine (C-C motif) ligand 5 (CCL5) in the hypothalamus by delivering an antagonist, MetCCL5, into the mouse brain using a micro-osmotic pump brain infusion system. This transient inhibition of CCL5 activity interrupted hypothalamic insulin signaling, leading to glucose intolerance and peripheral systemic insulin sensitivity.

Insulin regulates systematic metabolism in the hypothalamus and the peripheral insulin response. An inflammatory reaction in peripheral adipose tissues ...

Phase Contrast Magnetic Resonance Imaging in the Rat Common Carotid Artery

Phase contrast magnetic resonance imaging (PC-MRI) is a noninvasive approach that can quantify flow-related parameters such as blood flow. Previous studies have shown that abnormal blood flow may be associated with systemic vascular risk. Thus, PC-MRI can facilitate the translation of data obtained from animal models of cardiovascular diseases to pertinent clinical investigations. In this report, we describe the procedure for measuring blood flow in the common carotid artery (CCA) of rats using cine-gated PC-MRI and discuss relevant analysis methods. This procedure can be performed in a live, anesthetized animal and does not require euthanasia after the procedure. The proposed scanning parameters yield repeatable measurements for blood flow, indicating excellent reproducibility of the results. The PC-MRI procedure described in this article can be used for pharmacological testing, pathophysiological assessment, and cerebral hemodynamics evaluation.

The overall goal of this procedure is to measure the blood flow in the rat common carotid artery by using noninvasive phase contrast magnetic resonance imaging.

Phase contrast magnetic resonance imaging (PC-MRI) is a noninvasive approach that can quantify flow-related parameters such as blood flow. Previous ...

Endotoxin Activity Assay for the Detection of Whole Blood Endotoxemia in Critically Ill Patients

Lipopolysaccharide, also known as endotoxin, is a fundamental component of gram-negative bacteria and plays a crucial role in the development of sepsis and septic shock. The early identification of an infectious process that is rapidly evolving to a critical illness might prompt a quicker and more intensive treatment, thereby potentially leading to better patient outcomes. The Endotoxin Activity (EA) assay can be used at the bedside as a reliable biomarker of systemic endotoxemia. The detection of elevated endotoxin activity levels has been repeatedly shown to be associated with an increased disease severity in patients with sepsis and septic shock. The assay is quick and easy to perform. Briefly, after sampling, an aliquot of whole blood is mixed with an anti-endotoxin antibody and with added LPS. Endotoxin activity is measured as the relative oxidative burst of primed neutrophils as detected by chemioluminescence. The assay&#39;s output is expressed on a scale from 0 (absent) to 1 (maximal) and categorized as &#8220;low&#8221; (&#60;0.4 units), &#8220;intermediate&#8221; (0.4&#8211;0.59 units), or &#8220;high&#8221; (&#8805;0.6 units). The detailed methodology and rationale for the implementation of the EA assay are reported in this manuscript.

We hereby present a protocol to measure at the bedside the endotoxin activity of human whole blood samples. The Endotoxin Activity assay is a simple test to perform and may be a useful biomarker in critically ill patients with sepsis.

Lipopolysaccharide, also known as endotoxin, is a fundamental component of gram-negative bacteria and plays a crucial role in the development of sepsis ...